Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
Which Azure AI service is purpose-built for detecting anomalies in time series data?
⚠ Common exam trap
Microsoft often tests the distinction between a purpose-built service (Anomaly Detector) and a general-purpose platform (Azure Machine Learning), leading candidates to incorrectly choose the latter because they assume custom models are always required for anomaly detection.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Anomaly Detector
Azure AI Anomaly Detector is a dedicated, pre-built service specifically designed to identify anomalies in time series data without requiring custom model training. It uses machine learning algorithms to automatically detect spikes, dips, or pattern changes in sequential data, making it the correct choice for this purpose.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Machine Learning with custom anomaly detection models
Why it's wrong here
Azure Machine Learning can build custom anomaly detection models, but this requires choosing algorithms, performing feature engineering, training/evaluating classifiers, and deploying endpoints. That workflow assumes data science and ML expertise. Azure AI Anomaly Detector avoids this entirely by providing a pretrained managed service that scores anomalies in time series data automatically.
- ✓
Azure AI Anomaly Detector
Why this is correct
Azure AI Anomaly Detector is a managed cognitive service that applies statistical and deep learning models to univariate and multivariate time series. It automatically selects the best model, returns anomaly scores, expected values, and change-point alerts, and requires no custom training. Users simply send time-stamped numeric data to the API and interpret the returned anomaly flags.
- ✗
Azure AI Language sentiment analysis
Why it's wrong here
Azure AI Language sentiment analysis inspects unstructured text and assigns positive, negative, or neutral sentiment scores based on linguistic and semantic cues. It cannot process numeric time series values, so it cannot detect statistical outliers such as abrupt spikes or dips. Anomaly detection is specifically a data-analysis task, not a text-analysis task.
- ✗
Azure AI Vision spatial analysis
Why it's wrong here
Azure AI Vision spatial analysis uses video input to track people, detect occupancy, and monitor movement or interactions in physical spaces. This requires camera feeds, computer vision models, and bounding-box/pose logic. It is not designed to consume numeric time series telemetry, so it cannot identify anomalies in metrics like CPU usage or sensor readings.
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Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.